Research project objectives/ Research hypothesis The objective of this research is creating the supporting software to assist pathologist in evaluation of immunohistochemically stained tissue samples. Created technique will be tested on samples of breast cancer tissue stained with DAB&H (3,3' Diaminobenzidine&Haematoxylin) chosenfrom the tissue microarray. Developed method will be compared to the other known similar methods as well as to the manual evaluation of the pathologist.

Research project methodologyFor the purpose of this research we intend to use the acknowledged methods of image processing and segmentation as well as our own innovative means. Apart from that, neural networks and techniques of parallel computing is planned to be used. To achieve segmentation of the immunopositive and immunonegative objects the variety of thresholding algorithms will be tested. We will focus on the locally adaptive thresholding methods where local threshold is calculated at every point of image while threshold value is based on the intensity of the pixel and its neighborhood. It seems to be the most appropriate approach in view of the fact that there are contrast fluctuations between objects of interest and background across image plane and from image to image. We already tested some methods and acquired good results. Although, the immunohistochemically stained tissue samples are acquired as 3-channel RGB images, we plan to separate specific dye from the image with the colour deconvolution algorithm. The conversion of images to the other colour model such as HSV or Lab will be considered as well.Apart from the artificial images we will use experimental data collected from the tissue microarrays. To evaluate the whole sample size the method of selecting regions of interest will be developed next. We assume that the neural network will be able to make the decision upon chosen set of features such as texture, intensity variation, densitometry, etc, taken from the fragment of the image. The selected fragments are divided into parts which overlap, than parallel analysis is done, finally the algorithm eliminates unnecessary parts of results from the final objects maps and counting results.In final stage the project the proposed method results are compared to the known methods results treated as ‘golden standard’. Because we try to achieve most accurate results that can be relied on, only the objects that our system is "sure" is automatically classified as positive or negative while other objects are presented to pathologist who by one click make decision. The system will be memorizing the manual grading so it could be upgraded manually or even improved interactively with every new case. To conclude this research the graphical user interface will be made so that nonprogrammer user can use this software.

Expected impact of the research project on the development of science, civilization and societyImmunohistochemically stained tissue samples are used by pathologists to establish the diagnosis, the prognosis and the treatment in various types of cancer. The evaluation process takes into account the amount of immunopositive objects and the architecture of the tissue sample. Such evaluation can be done by the experienced pathologist directly via microscope or from digital images of the samples. The human direct evaluation is irreproducible, time-consuming as well as intra- and inter-observer error prone. On the other hand automated methods, based on the digital image processing can improve the evaluation. The results are reproducible and they can become foundation of inter- and intra-laboratory unification in cut-offs and threshold levels. There are some more or less effective automatic and semi-automatic methods which extract immunopositive and immunonegative objects from images acquired from chosen by operator part of tissue sections but effective solutions developed for tissue microarrays are still not available.The main problem is that there is no effective method that automatically selects regions of interest, which are areas where pathologist expects cancer cells without tissue not influenced by the disease and artifacts caused by sample preparation process. In this project the approach based on neural networks is proposed because of the power of this methodology. All this leads to make process of diagnostics more efficient and reliable and consequently more cancer patients overcome and recover (remission or survive).